Tootfinder

Been working on n-dimensional sampling/filtering functions for https://thi.ng/tensors and created an interactive Desmos graph to illustrate 1D Lanczos filtering/interpolation:
http…

1D/2D/3D/4D tensors with extensible polymorphic operations and customizable storage

Tensor-Tensor Products, Group Representations, and Semidefinite Programming
Alex Dunbar, Elizabeth Newman
https://arxiv.org/abs/2507.12729 https://

Tensor-Tensor Products, Group Representations, and Semidefinite Programming
The $\star_M$-family of tensor-tensor products is a framework which generalizes many properties from linear algebra to third order tensors. Here, we investigate positive semidefiniteness and semidefinite programming under the $\star_M$-product. Critical to our investigation is a connection between the choice of matrix M in the $\star_M$-product and the representation theory of an underlying group action. Using this framework, third order tensors equipped with the $\star_M$-product are a natural…

Explicit construction of the energy-momentum tensor in the large N limit
Carlo Pagani, Hidenori Sonoda
https://arxiv.org/abs/2508.11506 https://arxiv.org/p…

Explicit construction of the energy-momentum tensor in the large N limit
We construct the energy-momentum tensor of the O(N) linear sigma model explicitly in the large N limit. Our guiding principles behind the construction are twofold: first the energy-momentum tensor must satisfy the Ward identity for translation and rotation invariance, and second the energy-momentum tensor must have proper cutoff dependence as a composite operator. We verify that the energy-momentum tensor thus constructed satisfies the expected trace formula, and that the trace vanishes at the …

@… Hi, at the start of the year you collected bread knife measurements. What was that for?
https://digipres.club/@timixretroplays/1137563…

On definability of C*-tensor norms
Isaac Goldbring, Thomas Sinclair
https://arxiv.org/abs/2509.15086 https://arxiv.org/pdf/2509.15086

On definability of C*-tensor norms
We initiate the study of definability (in the model-theoretic sense) of C*-tensor norms. We show that neither the minimal nor maximal tensor norms are definable uniformly over all C*-algebras. The proof in the case of the minimal tensor product norm uses a deep theorem of Kirchberg characterizing exactness in terms of tensor products with matrix ultraproducts while the case of maximal tensor products uses Pisier's recent characterization of the lifting property in terms of maximal tensor produc…

Nvidia and Intel announce a partnership to develop multiple new generations of x86 products; Nvidia will purchase $5B of Intel stock at $23.28 per share (Paul Alcorn/Tom's Hardware)

Nvidia and Intel announce jointly developed 'Intel x86 RTX SOCs' for PCs with Nvidia graphics, also custom Nvidia data center x86 processors — Nvidia buys $5 billion in Intel stock in seismic deal
Cats and Dogs, living together!

So, does RIPA section 49 cover cryptocurrency wallets?
https://www.theguardian.com/uk-news/2025/sep/18/two-teenagers-charged-transport-for-london-cyber-attack

One, Two, Three: One empirical evaluation of a two-copy shadow tomography scheme with triple efficiency
Viet T. Tran, Richard Kueng
https://arxiv.org/abs/2508.11744 https://

One, Two, Three: One empirical evaluation of a two-copy shadow tomography scheme with triple efficiency
Shadow tomography protocols have recently emerged as powerful tools for efficient quantum state learning, aiming to reconstruct expectation values of observables with fewer resources than traditional quantum state tomography. For the particular case of estimating Pauli observables, entangling two-copy measurement schemes can offer an exponential improvement in sample complexity over any single-copy strategy conceivable [1, Huang, Kueng, Preskill, PRL(2021)]. A recent refinement of these ideas b…

Sanctions strike at Putin's fleet — 500 tankers on the "blacklist: https://benborges.xyz/2025/07/19/sanctions-strike-at-putins-fleet.html

A Nonparallel Support Tensor Machine for Binary Classification based Large Margin Distribution and Iterative Optimization
Zhuolin Du, Yisheng Song
https://arxiv.org/abs/2507.13012

A Nonparallel Support Tensor Machine for Binary Classification based Large Margin Distribution and Iterative Optimization
Based on the tensor-based large margin distribution and the nonparallel support tensor machine, we establish a novel classifier for binary classification problem in this paper, termed the Large Margin Distribution based NonParallel Support Tensor Machine (LDM-NPSTM). The proposed classifier has the following advantages: First, it utilizes tensor data as training samples, which helps to comprehensively preserve the inherent structural information of high-dimensional data, thereby improving class…